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As computer clusters are found to be highly effective for handling massivedatasets, the design of efficient parallel algorithms for such a computingmodel is of great interest. We consider ({\alpha}, k)-minimal algorithms forsuch a purpose, where {\alpha} is the number of rounds in the algorithm, and kis a bound on the deviation from perfect workload balance. We focus on new({\alpha}, k)-minimal algorithms for sorting and skew equijoin operations forcomputer clusters. To the best of our knowledge the proposed sorting and skewjoin algorithms achieve the best workload balancing guarantee when compared toprevious works. Our empirical study shows that they are close to optimal inworkload balancing. In particular, our proposed sorting algorithm is around 25%more efficient than the state-of-the-art Terasort algorithm and achievessignificantly more even workload distribution by over 50%.

({\alpha}, k)-Minimal Sorting and Skew Join in MPI and MapReduce